Legal AI, particularly when integrating general purpose machine learning models like ChatGPT, Claude, and other proprietary solutions, presents a double-edged sword. While these models promise enhanced efficiency, they also bring significant risks, especially concerning data privacy and compliance with legal standards.
UNDERSTANDING GENERATIVE AI IN LEGAL CONTEXTS
Generative AI systems are trained on public data to produce outputs that range from textual content and images to sophisticated code, and even interactive conversational agents. This technology’s potential extends to creating legal documents, for example drafting contracts or patent applications, formulating arguments and analysing database content. However, the use of AI in legal practice raises critical concerns about data confidentiality and the integrity of the legal process.
Legal AI and Confidentiality Concerns
While major players like Open AI, Microsoft, or Google are advancing their AI capabilities, the legal sector must treat such proprietary solutions very carefully. Legal professionals handle sensitive information requiring the utmost confidentiality, governed by strict regulations and codes of conduct. For instance, European Patent Attorneys are bound by the epi code of conduct, which stipulates that confidential information, such as an invention, must not be disclosed. This, of course, includes feeding confidential information in an AI chat bot.
Confidentiality Issues with Proprietary LLMs
Proprietary large language models as provided by major tech firms process data on their servers. This is of course very problematic for legal applications where client confidentiality is paramount. Even with assurances from AI providers, the risk of exposing sensitive client data on US servers persists, making these solutions unsuitable for confidential tasks like patent application drafting.
Therefore, any interaction with proprietary LLMs like ChatGPT, Claude, or Gemini is practically a no go in the legal industry, because the confidentiality of client communications and the security of their proprietary information are not just ethical obligations but are usually required by law.
The Dilemma of Wrapper Solutions (and Browser Extensions too)
One significant issue arises with wrapper solutions, where legal AI tools offer an interface to an API of a general-purpose machine learning model such as ChatGPT. While these solutions provide a layer of user-friendly interaction and do often hide the back-end language model used, they inherently risk exposing sensitive client information to third-party servers without any control on whether such data is used by the back-end model for training or where it is stored physically.
Nowadays, most legal AI services are in fact wrapper solutions that send client data via an API to a proprietary LLM where it may not be used for training purposes but is still stored outside the client’s jurisdiction with virtually no knowledge of or control over how his data may be utilized.
Particularly the numerous AI browser extension are wrapper solution that offer very little information or security. For example, when using a patent drafting AIaaS solution (AI as a Service AI), the inadvertent disclosure of an invention to the AIaaS provie and/or the back-end model potentially violates confidentiality agreements and professional codes of conduct.
REGULATORY FRAMEWORK AND COMPLIANCE
THE EU’S GDPR AND AI ACT
Addressing Transparency, Explainability, and Bias
The General Data Protection Regulation (GDPR) is a comprehensive European Union regulation that regulates protection of personal data privacy by setting stringent data handling requirements for organizations. By this, the GDPR ensures individuals’ rights over their data, and imposing heavy penalties for non-compliance.
Apparently, generative AI systems, often described as “black boxes,” pose challenges in transparency and explainability—crucial components under GDPR. Law firms that use or a third-party AI service, must explain to their clients how the AI is used and ensure that systems operate fairly and without inherent biases that could influence legal outcomes.
As addressed above, the use of external servers by AI service providers poses a complex challenge. Despite assurances of data security and privacy compliance, the transfer and storage of sensitive information on external servers can never be entirely risk-free. This scenario raises serious legal questions about adherence to the principles of client confidentiality and data protection regulations under the GDPR in the EU or HIPAA in the US.
Challenges of Data Subject Rights in AI
Fulfilling GDPR rights like access, rectification, and erasure is particularly challenging with generative AI due to the difficulty in tracing individual data points within the AI models. In the legal use of generative AI, under GDPR, law firms act as data controllers, obliging them to ensure any data processing upholds the privacy rights and interests of their clients. In other words, law firms are required to ensure that, whatever AI service they use, it aligns with the stringent GDPR framework, which, in the case of proprietary general-purpose LLMs is virtually impossible.
The European legal framework governing AI is in flux, with significant developments like the EU AI Act proposing a risk-based regulatory approach. Moreover, sector-specific laws in employment, healthcare, and finance also touch upon AI application, underscoring the need for a clear understanding of how these laws interact with generative AI technologies and what law firms can and cannot do.
Privacy Risks and Legal AI
The use of external AI solutions like general-purpose LLMs can result in unauthorized data exposure, e.g. for the following reasons:
- Training and Data Handling: Generative AI’s reliance on extensive data pools for training, including potentially sensitive information, poses risks of data breaches and misuse.
- Data Control and Processing: Legal professions must ensure that any generative AI solutions employed comply with GDPR’s strict protocols on data handling, particularly with regards to “order processing” or “contract data processing” without explicit client consent.
Order processing under the GDPR involves the processing of personal data by a third-party service provider, the generative AI provider, on behalf of the data controller, the law firm, where the service provide must process the data exclusively based on the instructions and under the control of the data controller. To comply with this critical concept under GDPR, the relationship between the data controller and the processor must be governed by a legally binding Data Processing Agreement (DPA), which outlines the scope, nature, and purpose of the processing, the responsibilities and obligations of the processor, and the rights of the data subjects.
How to Ensure Confidentiality
Ensuring Compliance through Localized AI Solutions
For European legal AI users, adhering to GDPR and impending regulations like the EU AI Act is of course non-negotiable. The safest route, of course, involves employing LLMs that run on in-house servers. Besides that, the following solutions are viable for European legal AI users:
- GDPR-Compliant Hosting of a General-Purpose Model: Employing a (proprietary) general-purpose LLMs hosted on servers within the EU, ensuring adherence to GDPR.
- GDPR-Compliant Hosting of a Customized Model: Using an AI service provider who offers via his front-end access to a specific-purpose back-end LLMs hosted on servers within the EU, which also ensures adherence to GDPR and the EU AI Act.
For adherence to the EU AI Act, the particular AI application must be observed and risk-classified, in order to assume appropriate measures as required by the AI Act. This approach not only complies with stringent EU data protection laws but also serves as a strong argument for clients choosing a law firm due to the added security and confidentiality assurances.
The Call for Strict Compliance and Oversight
For legal professionals, the integration of AI into their practices demands rigorous scrutiny and a proactive approach to compliance. Considerations include:
- Transparency: Legal AI tools must be transparent in how they process and handle data. Clients should be informed about the AI’s functionality and the extent of data sharing involved.
- Regular Audits: Frequent audits of AI tools and their providers can ensure compliance with both ethical standards and legal requirements. This includes reviewing how data is stored, processed, and deleted.
- Customized AI: Developing bespoke AI solutions controlled entirely by the legal firm can mitigate risks associated with third-party services. These solutions offer the advantage of customization to specific legal standards and client needs without the risk of external data exposure.
- Data Protection Impact Assessments (DPIA): Before implementing any AI technology, conducting a DPIA can help identify and minimize data protection risks associated with AI operations.
If local solutions are not appropriate: When incorporating AI into legal practices, extensive due diligence is necessary. Contracts with AI providers, such as Data Processing Agreements, must clearly define roles, responsibilities, and data handling processes, including who has access to the data and how it is protected. Additionally, law firms should establish clear terms with clients regarding the use of AI, ensuring informed consent and transparency.
CONCLUSION: Strategic Steps Forward
The significant privacy and confidentiality risks that generative AI comes with, must be carefully managed. By focusing on local and in-house AI solutions and ensuring transparency and client consent, law firms can enjoy the benefits of AI while upholding their legal responsibilities. Practically, a a best practice for AU usage by law firms should include the following steps:
- Risk Assessment: Conduct data protection impact assessments (DPIAs) to identify and mitigate risks associated with using AI.
- Transparency and Clear Client Communication: Develop transparent privacy policies clearly detailing how client data is used and the potential risks involved. According to the policies adopted, ensure clients are fully informed of how their data is handled.
- Prioritize AI solutions that are hosted on servers in the EU, if in-house implementations are not an option.
- Continuous Training and Updates: Keep legal teams updated on AI developments and ensure they understand the ethical implications and technological limits.
In conclusion, legal professionals must protect client information and navigate risks of disclosure when using external AI systems. Compliance with data protection regulations like the EU’s GDPR and the AI Act is crucial, requiring carefulness, transparency and fairness in AI use. To address these challenges, prioritize AI solutions on in-house servers or GDPR-compliant EU providers. Customized AI models and regular audits enhance security and compliance. Legal professionals must carefully adopt AI in legal practice to balance benefits with privacy, confidentiality, and regulatory requirements.
To excel in the legal field, stay updated on AI in law. Engage with peers, join events, seek advice from AI experts. By being informed, you can integrate AI responsibly, prioritizing client confidentiality and compliance. Let’s embrace the potential of AI in law while upholding trust and integrity. Ready to navigate AI challenges? Act now for a secure legal future.
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